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Record W2981275953

Different Paths? Human Capital Prices, Wages and Inequality in Canada and the U.S

2017· preprint· en· W2981275953 on OpenAlexaboutno aff
John Knight, Shi Li, Haiyuan Wan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInequalityNational wealthWealth elasticity of demandEconomic inequalityDifferential (mechanical device)MarketizationChinaIncome inequality metricsLabour economicsInflation (cosmology)Capital (architecture)Human capitalSample (material)Demographic economicsMarket economyGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

The inequality of wealth in China has increased rapidly in recent years. Prior to 1978 all Chinese households possessed negligible wealth. China therefore presents a fascinating case study of how inequality of household wealth increases as economic reforms take place, marketization occurs, and capital accumulates. Wealth inequality and its growth are measured and decomposed using data from two national sample surveys of the China Household Income Project (CHIP) relating to 2002 and 2013. Techniques are devised and applied to measure the sensitivity of wealth inequality to plausible assumptions about the under-representation of and the under-reporting by the wealthy. An attempt is made to explain the rising wealth inequality in terms of the relationships between income and wealth, house price inflation, differential savings, and income from wealth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.335
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in Economics→Same topicCanadian Policy and Governance→French-language works237,207→